Dimensionality Reduction
A data transformation technique that reduces the number of input variables while preserving the most important structural information and underlying patterns.
Think of It Like This
Like summarizing a dense 500-page novel into a 10-page plot outline that captures all the main characters and key events without the extra fluff.
High-dimensional datasets often suffer from the curse of dimensionality, making them difficult to visualize and computationally expensive to process. Methods like PCA, t-SNE, and UMAP project data into a lower-dimensional space by discarding redundant features. This not only speeds up training but can also act as a form of noise filtering.